code: def test_categorical_dist_kl_div():
      n_classes = 3
      dist_a = CategoricalDist(
          n_classes=n_classes, probs=mx.nd.array([0.4, 0.2, 0.4])
      )
      dist_b = CategoricalDist(
          n_classes=n_classes, probs=mx.nd.array([0.3, 0.4, 0.3])
      )
      dist_c = CategoricalDist(
          n_classes=n_classes, probs=mx.nd.array([0.2, 0.6, 0.2])
      )
      dist_d = CategoricalDist(
          n_classes=n_classes, probs=mx.nd.array([0.0, 1.0, 0.0])
      )
      np.testing.assert_almost_equal(
          actual=dist_a.kl_div(dist_b).asnumpy(), desired=0.09151624
      )
      np.testing.assert_almost_equal(
          actual=dist_a.kl_div(dist_c).asnumpy(), desired=0.33479536
      )
      np.testing.assert_almost_equal(
          actual=dist_c.kl_div(dist_a).asnumpy(), desired=0.38190854
      )
      np.testing.assert_almost_equal(
          actual=dist_a.kl_div(dist_d).asnumpy(), desired=np.nan
      )
      np.testing.assert_almost_equal(
          actual=dist_d.kl_div(dist_a).asnumpy(), desired=1.60943782
      )
  
imports: import mxnet
  import numpy
  import os
  import pytest
  from scipy import stats
  import sys
  from rl_coach.architectures.head_parameters import PPOHeadParameters
  from rl_coach.architectures.mxnet_components.heads.ppo_head import CategoricalDist, MultivariateNormalDist, DiscretePPOHead, ClippedPPOLossDiscrete, ClippedPPOLossContinuous, PPOHead
  from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters
  from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace
label_desc: Use image node_id 10 for calling a global function with example usage: test_categorical_dist_kl_div() without return types